Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes well beyond the annotations by disclosing refusal behavior, the exact refusal_reason enum, the return shape with evidence and confidence, and the fact that it costs an extra LLM call. It also clarifies that the tool will not invent facts when data is insufficient, which is critical behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: mode definition, routing behavior, return/refusal contract, and usage guidance with cost tradeoff. It is front-loaded with the core purpose and avoids fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description defines the success return shape and all refusal reasons, making the tool's behavior predictable. It also covers routing, constraints, and cost tradeoffs, so an agent has enough context to invoke it correctly and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the schema already documents the single meaningful parameter 'question' plus five aliases. The description adds no new parameter semantics, but the schema carries the full burden, so a baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a specific behavior: a hallucination-resistant grounded answer mode that routes through Pipeworx and extracts answers only from tool results. It differentiates itself from siblings ask_pipeworx and ask_pipeworx_beta by emphasizing grounded extraction, refusal behavior, and higher cost.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: whenever an answer will be quoted, cited, or acted on, especially in high-stakes domains. It also gives a clear alternative: prefer ask_pipeworx for casual lookups, which gives the agent actionable selection criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.